Comparative Effectiveness Research: Challenges for Medical Journals
Bibliographic record
Abstract
In order to optimize health outcomes within the constraints of inevitably limited resources, low- and high-income countries alike require unbiased means of assessing health care interventions for their relative effectiveness. Such interventions include diagnostic tests and treatments (both established and newly developed) and implementation of health policy [ [1] Eden J, Wheatley B, McNeil B, Sox H, Editors; Committee on Reviewing Evidence to Identify Highly Effective Clinical Services, Institute of Medicine. (2008) Knowing What Works in Health Care: A Roadmap for the Nation. Washington, DC: National Academy Press. Available: http://www.nap.edu/catalog.php?record_id=12038. Accessed 26 March 2010. Google Scholar ]. Likewise, health care professionals and patients need better information to inform health care decisions that require weighing benefits and risks in light of the patient's medical history and personal preferences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.777 | 0.902 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.007 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.033 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".